Route detection method and apparatus, device, storage medium and computer program product

Abstract

A route detection method, comprising: acquiring a target navigation route to be tested, the target navigation route comprising a plurality of sequentially connected road segments (502); on the basis of the plurality of sequentially connected road segments, determining a plurality of pairs of adjacent road segments (504); on the basis of historical navigation routes, querying the pass frequency for every two adjacent road segments within a specified time range (506); and, on the basis of the pass frequency for every two adjacent road segments within the specified time range, obtaining a pass frequency sequence corresponding to the target navigation route, and, on the basis of the pass frequency sequence, determining whether the target navigation route passes through abnormal road segments (508).

Description

Route detection method, device, equipment, storage medium and computer program product

[0001] This application claims priority to a Chinese patent application filed with the Patent Office of China on March 14, 2024, with application number 202410309879.1 and application name “Route Detection Method, Device, Equipment, Storage Medium and Computer Program Product,” the entire contents of which are incorporated herein by reference. Technical Field

[0002] The present application relates to the field of computer technology, and in particular to a route detection method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0003] With the development and progress of computer technology and Internet technology, users can use map navigation applications for route navigation before traveling. However, if some abnormal situations occur temporarily on the actual road, such as temporary road closures, traffic accidents, etc., it is impossible to avoid the road sections where these abnormal situations occur in time when generating the navigation route, and the rationality of route planning is poor. When the user uses the navigation route for route navigation, when passing through the road sections with these abnormal situations, the user has to temporarily change the route to detour, which not only wastes the computing resources and energy consumption of the navigation equipment, but also may increase the extra travel time, causing great inconvenience to the user and resulting in a poor user experience. Summary of the Invention

[0004] According to various embodiments of the present application, a route detection method is provided. The method includes:

[0005] Acquire a target navigation route to be detected, where the target navigation route includes a plurality of sequentially connected road sections;

[0006] Determining a plurality of pairs of adjacent road sections based on the plurality of sequentially connected road sections;

[0007] Querying the number of times each of the two adjacent road sections passes within a specified time range based on historical navigation routes;

[0008] A passage number sequence corresponding to the target navigation route is obtained based on the number of times each of the two adjacent road sections passes through within a specified time range, and it is determined based on the passage number sequence whether the target navigation route passes through an abnormal road section.

[0009] According to various embodiments of the present application, a route detection device is provided. The device includes:

[0010] An acquisition module, configured to acquire a target navigation route to be detected, wherein the target navigation route includes a plurality of sequentially connected road sections;

[0011] an adjacent road segment determining module, configured to determine a plurality of pairs of adjacent road segments based on the plurality of sequentially connected road segments;

[0012] A travel frequency query module is used to query the travel frequency of each of the two adjacent road sections within a specified time range based on the historical navigation route;

[0013] The anomaly detection module is used to obtain a passage number sequence corresponding to the target navigation route based on the passage number of each of the two adjacent road sections within a specified time range, and determine whether the target navigation route passes through an abnormal road section based on the passage number sequence.

[0014] According to various embodiments of the present application, a computer device is provided, comprising a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned route detection method when executing the computer-readable instructions.

[0015] According to various embodiments of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the route detection method described above.

[0016] According to various embodiments of the present application, a computer program product is provided, which includes computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the route detection method are implemented.

[0017] The details of one or more embodiments of the present application are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] FIG1 is a diagram illustrating an application environment of a route detection method in some embodiments;

[0020] FIG2 is a schematic diagram of a navigation route;

[0021] FIG3 is an enlarged view of a local section of a navigation route;

[0022] Figure 4 is an enlarged view of the traffic flow change trajectory distribution diagram;

[0023] FIG5 is a schematic flow chart of a route detection method in some embodiments;

[0024] FIG6 is a schematic diagram of a process for counting the number of passes of adjacent road segments in some embodiments;

[0025] FIG7 is a schematic diagram of navigation routes in some other embodiments;

[0026] FIG8 is a schematic diagram of navigation routes in yet other embodiments;

[0027] FIG9 is a schematic diagram of the overall framework of route correction in some embodiments;

[0028] FIG10 is a schematic diagram of a route before and after correction in some embodiments;

[0029] FIG11 is a block diagram of a route detection device in some embodiments;

[0030] FIG12 is a diagram illustrating the internal structure of a computer device in some embodiments. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0032] The route detection method provided in the embodiment of the present application can be applied to the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other servers. In some embodiments, the server 104 obtains a target navigation route generated based on the starting position and the end position, and the target navigation route includes a plurality of sequentially connected road sections. Based on the plurality of sequentially connected road sections, multiple pairs of adjacent road sections are determined. The number of passes of the adjacent road sections within a specified time range is queried based on the historical navigation route. According to the number of passes of each adjacent road section within the specified time range, a pass number sequence corresponding to the navigation route is obtained, and it is judged whether the target navigation route passes through an abnormal road section based on the pass number sequence.

[0033] Terminal 102 may be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include intelligent voice interaction devices, smart TVs, smart air conditioners, smart vehicle-mounted devices, aircraft, etc. Portable wearable devices may include smart watches, smart bracelets, head-mounted devices, etc. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers. The embodiments of the present application can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, and assisted driving.

[0034] Map road network data contains a large number of road segments (links). A road segment is the smallest unit in road network data, and map road network data can be divided into hundreds of millions of road segments. Multiple topologically connected road segments constitute a route, that is, a route is composed of a string of links. When two road segments are topologically connected, for example, link1 is connected to link2, and the route direction is from link1 to link2, the number of vehicles passing from link1 to link2 within a specified time range (such as an hour, a day, a week, or a month) can be called the vehicle traffic flow from link1 to link2 within the specified time range.

[0035] In some embodiments, to promptly identify abnormal road sections in a road network that are impassable (e.g., temporarily closed or blocked), anomalies can be detected based on the traffic volume on each road section in the road network. For example, if the traffic volume on a road section has been very low for a long time, or if the traffic volume on a road section has recently dropped sharply, then this road section may be identified as an abnormal road section. Because this approach fails to integrate the target navigation route, the detection effect is poor and its impact on the planned navigation route is limited. For example, if the traffic volume on a certain road section is neither particularly high nor particularly low based on traffic volume data, it can be identified as a side road. In this case, the navigation route planning process will likely not avoid this road section. However, if the traffic volume on the adjacent road section is several times (e.g., 20 times) higher than the traffic volume on the road section, it is likely that this road section has an abnormality. Failure to avoid this road section during navigation route planning will result in the planned navigation route not meeting the user's needs, not only wasting computing resources and energy consumption of the route navigation device, but also potentially increasing travel time, causing significant inconvenience to the user and resulting in a poor user experience.

[0036] The route detection method provided in the embodiment of the present application, when determining whether the target navigation route passes through an abnormal section, is not based on the number of passes of each section, but on the flow change trajectory of the target navigation route (reflected by the sequence of the number of passes) to identify the abnormal section. In particular, when the number of passes of the section fails to reflect the abnormality, the abnormal section can be detected through the flow change trajectory, so that the abnormal route in the target navigation route can be corrected, thereby improving the rationality and accuracy of route planning, avoiding the waste of computing resources and energy consumption of the route navigation equipment caused by unreasonable navigation route planning, and improving the user experience of route navigation.

[0037] Generally speaking, navigation starts from a low-level road at the starting point, then moves to a high-level road, then back to a low-level road near the end point. This means that traffic flow from the starting point to the end point generally follows a low-to-high-to-low trend. For example, if you enter a highway from Area A and then from the highway to Area B, the traffic flow on the highway will be higher than on the highway. If the traffic flow for a navigation route changes from a high-traffic road to a low-traffic road with a sharp drop in traffic, and then returns to a high-traffic road, it is likely that the planned navigation route is abnormal. Figure 2 shows a schematic diagram of a navigation route that goes from the starting point to the highway, then exits the highway and reaches the end point. Figure 3 is a magnified view of a section of the route, showing the exit and entry points to the highway. Figure 4 shows a zoomed-in map of traffic flow changes. Road sections with denser dots represent higher traffic flow. This zoomed-in map shows a situation where navigation moves from a high-traffic road to a low-traffic road and then back to a high-traffic road. On the right side, there is another route with higher traffic flow, meaning it's easier to navigate. The route detection method provided in this embodiment of the application can identify this localized abnormal section and perform route repairs.

[0038] In some embodiments, as shown in FIG5 , a route detection method is provided. The method is described by taking the application of the method to the server 104 in FIG1 as an example, and includes the following steps:

[0039] Step 502: Acquire a target navigation route to be detected, where the target navigation route includes a plurality of sequentially connected road sections.

[0040] The target navigation route is a navigation route obtained by route planning based on the starting point and the end point, and the target navigation route includes a plurality of sequentially connected sections. In some embodiments, the terminal may send a navigation route generation request to a server providing map navigation services based on the starting point and the end point input by the user, and the server may generate at least one navigation route based on the received navigation route generation request, and the target navigation route may be one of the at least one navigation route. The server may perform anomaly detection on the target navigation route. In some embodiments, for the target navigation route detected to have an abnormal section, the server may correct it, obtain a corrected navigation route, and return it to the terminal. In some embodiments, for the target navigation route detected to have an abnormal section, the server may also filter out the target navigation route with the abnormal section from the at least one navigation route, and return the remaining navigation route to the terminal.

[0041] Step 504: Determine multiple pairs of adjacent road sections based on multiple sequentially connected road sections.

[0042] The target navigation route obtained after the navigation route planning is completed is a relatively optimal path between the starting and ending points, which includes multiple sections connected in sequence, that is, multiple sections are connected in sequence according to a topological relationship. Based on this, the server can determine multiple pairs of adjacent sections according to the multiple sections included in the target navigation route. Optionally, the server can traverse the sections passed by the target navigation route in sequence, and construct the current traversed section and the next traversed section into a pair of adjacent sections until the traversal is completed when the current traversed section is the second to last section. Optionally, the server can start from the second traversed section and construct the current traversed section and the previous traversed section into a pair of adjacent sections until the traversal is completed when the current traversed section is the first to last section.

[0043] For example, the target navigation route is a route formed by topologically connecting link1, link2, link3, link4, and link5 in sequence. The server can determine that the multiple pairs of adjacent road segments included in the target navigation route are: link1-link2, link2-link3, link3-link4, link4-link5 in sequence.

[0044] Step 506: query the number of times two adjacent road sections have been traveled within a specified time range based on the historical navigation routes.

[0045] Historical navigation routes are routes generated and completed for vehicle navigation in the past. For historical navigation routes within a certain period of time, the server can store the corresponding travel counts. Specifically, the "past period" can be the past 30 minutes, past hour, past day, past week, past month, past three months, and so on, with no specific time range restrictions. The server can query the travel counts of two adjacent road segments within a specified time range based on historical navigation routes within that time range.

[0046] As shown in FIG6 , in some embodiments, in order to periodically update the number of trips on two adjacent road segments within a specified time range and ensure the validity and timeliness of the trip number data, the server may periodically (e.g., every 30 minutes, every hour, every day, every month, etc.) perform the following steps:

[0047] Step 602: Acquire a set of historical navigation routes within a specified time range.

[0048] In order to ensure the timeliness of traffic flow data as much as possible, the server can set execution cycles and specified time ranges for different regions. For example, for hot areas such as cities, the server can be set to obtain a collection of historical navigation routes within the past hour every 30 minutes, or to obtain a collection of historical navigation routes within the past hour every hour. For areas with low traffic volume such as the outer ring road of the city, the server can be set to obtain a collection of historical navigation routes within the past hour every 2 hours. For other areas with less traffic volume, such as remote areas, it is set to obtain a collection of historical navigation routes within the past month every week. For the historical navigation routes in the historical navigation route set, the server also needs to obtain the number of navigations for each historical navigation route within the specified time range. For example, in the past period of time, the same historical navigation route was used by multiple users and the number of completed navigations was 200 times, then the corresponding number of navigations is 200.

[0049] Step 604: Decompose each historical navigation route in the historical navigation route set to obtain two adjacent road segments included in the historical navigation route.

[0050] Step 606: Obtain a set of pairwise adjacent road sections based on the decomposed pairwise adjacent road sections.

[0051] In order to determine the number of passes between two adjacent road sections and accurately reflect the change trend of the traffic flow on the road sections, the server can disassemble each historical navigation route in the historical navigation route set to obtain the two adjacent road sections included in the historical navigation route.

[0052] For example, the historical navigation route set obtained by the server within a specified time range includes 5 historical navigation routes, namely:

[0053] Historical navigation route 1: link1, link2, link3, link4, link5, the number of navigations is 500;

[0054] Historical navigation route 2: link3, link4, link5, link6, link7, the number of navigations is 100;

[0055] Historical navigation route 3: link1, link2, link5, link6, link7, navigation times is 50;

[0056] Historical navigation route 4: link1, link2, link4, link5, navigation times is 50;

[0057] Historical navigation route 5: link2, link3, link4, link5, navigation times is 300;

[0058] The two adjacent road sections that are disassembled include:

[0059] link1-link2;

[0060] link2-link3;

[0061] link3-link4;

[0062] link4-link5;

[0063] link5-link6;

[0064] link6-link7;

[0065] link2-link5;

[0066] link2-link4;

[0067] It is understandable that the above data are only for illustration, and a large number of historical navigation routes and a large number of adjacent road sections will be obtained during actual implementation.

[0068] Step 608 : For each pair of adjacent road segments in the set of adjacent road segments, the number of navigations of the historical navigation routes including the adjacent road segments within the specified time range is accumulated to obtain the number of travels of the adjacent road segments within the specified time range.

[0069] In the above example, we can determine the number of trips per pair of adjacent road segments within a specified time range:

[0070] link1-link2, the number of passes is 600;

[0071] link2-link3, the number of passes is 800;

[0072] link3-link4, the number of passes is 900;

[0073] link4-link5, the number of passes is 950;

[0074] link5-link6, the number of passes is 150;

[0075] link6-link7, the number of passes is 150;

[0076] link2-link5, the number of passes is 50;

[0077] link2-link4, number of passes: 50.

[0078] Following steps 602-608 above, the server periodically updates the number of trips between two adjacent road segments within a specified timeframe, ensuring the validity and timeliness of the trip count data. This allows the server to accurately count and store the number of trips between two adjacent road segments with topological relationships within the map network data within a specified timeframe. This number of trips reflects the vehicle traffic volume between the two adjacent road segments. Therefore, after obtaining multiple pairs of two adjacent road segments within a target navigation route, the server can query the corresponding number of trips within the specified timeframe based on the aforementioned statistical data. This makes the subsequently generated trip count sequence more accurate, thereby improving the accuracy of abnormal route detection.

[0079] Step 508: Obtain a passage number sequence corresponding to the target navigation route based on the passage number of each pair of adjacent road sections within a specified time range, and determine whether the target navigation route passes through an abnormal road section based on the passage number sequence.

[0080] In an embodiment of the present application, by arranging the number of passes of two adjacent sections included in the target navigation route in sequence within a specified time range, a pass number sequence corresponding to the target navigation route can be obtained. The pass number sequence reflects the traffic change trend of the target navigation route, and abnormal sections in the target navigation route can be detected through the traffic change trend. In particular, when the number of passes of a section fails to reflect the abnormality, the abnormal section can be detected through the traffic change trend.

[0081] According to the previous analysis, generally speaking, navigation starts from a low-level road at the starting point, navigates to a high-level road, and then navigates back to a low-level road near the end point. In other words, the traffic flow from the starting point to the end point generally changes from low to high to low. If the traffic flow change trend corresponding to the navigation route is navigating from a high-flow road to a low-flow road with a sharp drop in traffic, and then returns to a high-flow road, it can be considered that this planned navigation route is likely to be abnormal. Therefore, based on this, the server can determine the corresponding traffic flow change trend based on the traffic count sequence corresponding to the target navigation route, and compare the traffic flow change trend corresponding to the target navigation route with the traffic flow change trends corresponding to the normal route or the abnormal route to determine whether the target navigation route passes through an abnormal section.

[0082] In some embodiments, a pass count sequence corresponding to the target navigation route is obtained based on the number of times each pair of adjacent road sections passes through a specified time range, and whether the target navigation route passes through an abnormal road section is determined based on the pass count sequence, including: arranging the corresponding number of times each pair of adjacent road sections passes through a specified time range according to the order of each pair of adjacent road sections on the target navigation route to obtain a pass count sequence corresponding to the target navigation route; when the numerical change trend corresponding to the pass count sequence does not match the preset pass trajectory, it is determined that the target navigation route passes through an abnormal road section.

[0083] In this embodiment, the preset passage trajectory reflects the traffic flow change trend corresponding to the normal route. For example, the preset passage trajectory is a trajectory from a low-flow section to a high-flow section and then back to the low-flow section.

[0084] Specifically, after obtaining the pass count sequence corresponding to the target navigation route, the server can determine the numerical change trend corresponding to the pass count sequence, for example, whether it is a low value, a high value, and then a low value. The numerical change trend corresponding to the pass count sequence is matched with a preset pass trajectory. If the numerical change trend does not match the preset pass trajectory, it is determined that the target navigation route passes through an abnormal section. If the numerical change trend matches the preset pass trajectory, it is determined that the target navigation route does not pass through an abnormal section, that is, the target navigation route passes the abnormality detection.

[0085] In some embodiments, when the numerical change trend corresponding to the pass number sequence does not match the preset pass trajectory, the target navigation route is determined to pass through an abnormal section, including: for each pass number in the pass number sequence, obtaining the adjacent pass numbers on both sides of the current pass number in the pass number sequence; when the current pass number is less than a preset threshold and the adjacent pass numbers on both sides of the current pass number are greater than N times the current pass number, determining that the numerical change trend corresponding to the pass number sequence does not match the preset pass trajectory, and determining that the two adjacent sections corresponding to the current pass number are abnormal sections, where N is a number greater than 10.

[0086] The adjacent pass counts on either side of the current pass count are relatively adjacent pass counts in the pass count sequence, not necessarily directly adjacent pass counts. In other words, as long as there is a subsequence in the pass count sequence whose numerical trend does not match the preset pass trajectory, an abnormal road section exists in that subsequence. If the current pass count is less than a preset threshold and the adjacent pass counts on either side of the current pass count are greater than N times the current pass count, it can be determined that the numerical trend corresponding to the pass count sequence does not match the preset pass trajectory. The value of N can be determined based on actual data analysis; for example, N can be 20. For example, if the current pass count is 50, while the adjacent pass counts on both sides are 5000, a significant difference exists. This can indicate that an abnormality exists in the pairwise adjacent road sections corresponding to the current pass count, or in the later of the pairwise adjacent road sections.

[0087] For example, the target navigation route is: link1-link2-link4-link5. In the above example, it can be determined that the corresponding pass count sequence of the target navigation route is: 600-50-950. It can be seen that the target navigation route has experienced the corresponding value change area, which does not match the normal traffic flow from the starting point to the end point, which is generally low -> high -> low. It can be determined that there are abnormal sections in the target navigation route, such as temporary road closures.

[0088] In some embodiments, a pass count sequence corresponding to the target navigation route is obtained based on the number of times each pair of adjacent road sections passes through a specified time range, and whether the target navigation route passes through an abnormal road section is determined based on the pass count sequence, including: arranging the corresponding number of times each pair of adjacent road sections passes through a specified time range according to the order of each pair of adjacent road sections on the target navigation route to obtain a pass count sequence corresponding to the target navigation route; when the numerical change trend corresponding to the pass count sequence matches the preset pass trajectory, it is determined that the target navigation route passes through an abnormal road section.

[0089] In this embodiment, the preset passage trajectory reflects the traffic flow change trend corresponding to the abnormal route. For example, the preset passage trajectory is a trajectory from a high-flow section to a low-flow section and then back to a high-flow section.

[0090] Specifically, after obtaining the pass count sequence corresponding to the target navigation route, the server can determine the numerical change trend corresponding to the pass count sequence, for example, whether it is a high value to a low value and then to a high value, and match the numerical change trend corresponding to the pass count sequence with the preset pass trajectory. When the numerical change trend matches the preset pass trajectory, it is determined that the target navigation route passes through an abnormal section.

[0091] Figure 7 is a schematic diagram of a navigation route in one embodiment. In Figure 7, densely populated areas represent traffic-intensive areas, and white road sections represent the navigation route. This navigation route clearly passes through sections with zero traffic (i.e., sections indicated by arrows in Figure 7), indicating that this navigation route is not planned properly.

[0092] In some embodiments, the server may determine that the target navigation route passes through a "side road," i.e., a road section that is passable but has poor traffic capacity, if the current pass count is greater than a preset threshold and the adjacent pass counts on both sides of the current pass count are greater than M times the current pass count. If this type of road section is detected in the planned target navigation route, the server may prompt the terminal or display relevant prompt information for the user to select.

[0093] Figure 8 is a schematic diagram of a navigation route in one embodiment. In Figure 8, denser areas with small circles represent heavier traffic. The white line represents the navigation route. After the navigation route passes through a section with lower traffic (the section indicated by the arrow in Figure 8) and then returns to a section with heavier traffic, the server can determine that this section is a passable path.

[0094] In some embodiments, the route detection method further includes:

[0095] When the target navigation route passes through an abnormal section, an abnormal mark is marked for the abnormal section;

[0096] When the abnormal mark of the abnormal road section is not invalid, for the received navigation route generation request, route planning is performed on the navigation route generation request by avoiding the abnormal road section.

[0097] That is to say, for the abnormal road sections detected, the server can mark them with abnormal marks. For the navigation route generation requests received subsequently, if the abnormal marks of the abnormal road sections have not expired, the server can avoid these sections marked with abnormal marks when planning the route. In this way, the abnormality detection results can flow back to the map road network data, improving the problem of inaccurate or imperfect data in the map road network, thereby contributing to the rationality and perfection of subsequent navigation route planning and improving the user experience of route navigation. It can be understood that as new navigation routes are continuously generated, new traffic volume is also changing. For the same two adjacent road sections, the number of passes within a specified time range will also continue to change with periodic detection, and the corresponding abnormal marks may be marked as invalid.

[0098] In one embodiment, the route detection method further includes:

[0099] In the case that the target navigation route passes through an abnormal road section, the target navigation route is corrected to obtain a corrected navigation route, and the corrected navigation route does not include the abnormal road section.

[0100] In one embodiment, when the target navigation route passes through an abnormal section, the target navigation route is corrected to obtain a corrected navigation route, including: when the target navigation route passes through an abnormal section, determining the two endpoints corresponding to the abnormal section, regenerating a local path based on the two endpoints, and using the generated local path to replace the abnormal path in the target navigation route to obtain a corrected navigation route.

[0101] In this embodiment, for the target navigation route, if an abnormal path is detected, the path planning can be re-performed for the target navigation route based on the topological relationship of the remaining normal sections in the target navigation route in the road network to obtain a navigation correction route. The navigation correction route is more reasonable and can improve the navigation efficiency.

[0102] The above-mentioned route detection method obtains the target navigation route, determines multiple pairs of adjacent road sections included in the target navigation route, queries the number of passes of the adjacent road sections within a specified time range based on the historical navigation route, obtains the pass number sequence corresponding to the target navigation route based on the number of passes of each adjacent road section within the specified time range, and judges whether the target navigation route passes through an abnormal road section based on the pass number sequence. In other words, when judging whether the target navigation route passes through an abnormal road section, it is not based on the number of passes of each road section, but on the traffic change trajectory of the target navigation route (reflected by the pass number sequence) to identify the abnormal road section. In particular, when the number of passes of the road section fails to reflect the abnormality, the abnormal road section can be detected through the traffic change trajectory, thereby improving the rationality and accuracy of route planning, avoiding the waste of computing resources and energy consumption of the route navigation equipment caused by unreasonable navigation route planning, and improving the user experience of route navigation.

[0103] To more promptly detect sudden anomalies on the road, such as temporary road closures, in some embodiments, the server can use finer-grained traffic flow data to detect anomalies, such as historical navigation routes within a one-hour period. This is because the traffic volume on the abnormal road section may still be very high within a day, making it difficult to detect the anomaly. Using traffic flow data within a one-hour period can identify the anomaly. In other words, the more timely the traffic flow data, the better, such as historical navigation routes within half an hour, historical navigation routes within ten minutes, and so on. When the traffic flow within a fine-grained time range indicates an anomaly, it means that an abnormal event occurred within a relatively short period of time. When the traffic flow within a fine-grained time range indicates no anomaly, data within a longer time range can be used for anomaly detection.

[0104] In some embodiments, querying the number of times two adjacent road segments are passed within a specified time range based on historical navigation routes includes: querying the number of times two adjacent road segments are passed within a specified time range based on historical navigation routes within a first specified time range; and when it is determined that the target navigation route does not pass through an abnormal road segment based on the number of times each two adjacent road segments are passed within the first specified time range, continuing to query the number of times two adjacent road segments are passed within a specified time range based on historical navigation routes within a second specified time range; wherein the time granularity of the second specified time range is greater than the time granularity of the first specified time range.

[0105] In this embodiment, historical navigation route data within different specified time ranges is used to cover different areas. The time granularity of the second specified time range is greater than that of the first specified time range. For example, the second specified time range is daily traffic, i.e., historical navigation route data within a day, while the second specified time range is monthly traffic, i.e., historical navigation route data within a month. When the number of passes indicated by the historical navigation routes within the first specified time range is very high, there is no need to use the historical navigation routes within the second specified time range for detection. Otherwise, when the number of passes indicated by the historical navigation routes within the first specified time range is very low and an anomaly cannot be detected, the historical navigation route data with a longer time granularity, i.e., the historical navigation route data within the second specified time range, can be used to detect the problem. Similarly, in this way, anomaly detection of navigation routes in various regions of the map road network data can be covered. In particular, when the traffic volume is neither particularly high nor particularly low, but there is a significant difference in traffic volume from the adjacent road sections on both sides, anomalies can be identified, thereby improving the rationality of route planning.

[0106] In some embodiments, a pass count sequence corresponding to a target navigation route is obtained based on the pass counts of each pair of adjacent road segments within a specified time range, and determining whether the target navigation route passes through an abnormal road segment based on the pass count sequence includes:

[0107] Obtaining a first pass count sequence corresponding to the navigation route based on the number of passes of each pair of adjacent road sections within a first specified time range, and determining whether the target navigation route passes through an abnormal road section based on the first pass count sequence; and

[0108] When it is determined that the target navigation route does not pass through an abnormal section based on the first pass count sequence, a second pass count sequence corresponding to the navigation route is obtained based on the pass count of each pair of adjacent sections within the second specified time range, and whether the target navigation route passes through an abnormal section is determined based on the second pass count sequence.

[0109] According to the above method, the time granularity is different, and the number of passes corresponding to two adjacent sections is different. When multiple attempts are needed to detect anomalies using data within different time ranges, the server needs to determine the sequence of the number of passes corresponding to the target navigation route within different time ranges respectively, so as to accurately detect whether there are abnormal sections in the target navigation route.

[0110] In some embodiments, the route detection method further includes:

[0111] When it is judged that the target navigation route passes through an abnormal section based on the number of times each pair of adjacent road sections are passed within the first specified time range, the target navigation route is corrected by bypassing the abnormal section to obtain a corrected navigation route; and when it is judged that the target navigation route does not pass through an abnormal section based on the number of times each pair of adjacent road sections are passed within the first specified time range, and when it is judged that the target navigation route passes through an abnormal section based on the number of times each pair of adjacent road sections are passed within the second specified time range, the target navigation route is corrected by avoiding the abnormal section to obtain a corrected navigation route.

[0112] According to the above method, the time granularity is different, and the number of passes corresponding to two adjacent sections is different. When multiple attempts are needed to detect anomalies using data within different time ranges, the server needs to determine whether there are abnormal sections in the sequence of pass times corresponding to the target navigation route within different time ranges, so as to accurately detect whether there are abnormal sections in the target navigation route.

[0113] As shown in Figure 9, it is a schematic diagram of the overall framework of route correction in one embodiment. Referring to Figure 9, the first step is to integrate traffic offline. The traffic of road sections can be calculated by time granularity. The server counts the traffic of daily, weekly, monthly, quarterly and hourly sections to obtain the number of passes for each road section corresponding to different time granularities. Then, the online navigation service can load the above statistical data. When a route navigation request is received, the target navigation route is generated. Then, anomaly detection is performed, as shown in Figure 9, which specifically includes:

[0114] Step 902 , load the daily road traffic, and identify whether there are abnormal sections in the target navigation route. If not, execute step 904 ; if so, execute step 908 .

[0115] Step 904 , continue to load the weekly road traffic, identify whether there are abnormal sections in the target navigation route, if not, execute step 906 , if yes, execute step 908 .

[0116] Step 906, continue to load the monthly road traffic, identify whether there are abnormal sections in the target navigation route, if not, then end the identification and return to the recalled route, if so, execute step 908.

[0117] Step 908, determining abnormal road sections;

[0118] Step 910, avoid abnormal road sections and perform local route correction;

[0119] Step 912: Use the generated partial path to replace the abnormal path in the target navigation route to obtain a corrected navigation route;

[0120] Step 914: Mark the abnormal road section.

[0121] In this way, the abnormal road section can be avoided during subsequent route planning.

[0122] In a specific embodiment, the route correction method includes the following steps:

[0123] 1. Get a collection of historical navigation routes within a specified time range;

[0124] 2. Decompose each historical navigation route in the historical navigation route set to obtain the adjacent road segments included in each historical navigation route;

[0125] 3. Based on the disassembled pairwise adjacent road sections, a pairwise adjacent road section set is obtained;

[0126] 4. For each pair of adjacent road segments in the set of adjacent road segments, the number of navigations of the historical navigation routes including the adjacent road segments within the specified time range is accumulated to obtain the number of passages of the adjacent road segments within the specified time range.

[0127] 5. Obtain the navigation route of the target to be detected;

[0128] 6. Determine multiple pairs of adjacent road sections included in the target navigation route;

[0129] 7. Based on the historical navigation routes within the first specified time range, query the number of passes between two adjacent road sections within the specified time range;

[0130] 8. Arrange the number of trips of each pairwise adjacent road section within a first specified time range according to the order of their respective locations on the target navigation route to obtain a first trip count sequence corresponding to the target navigation route. If the numerical change trend of the first trip count sequence does not match a preset travel trajectory, the target navigation route is determined to pass through an abnormal road section. The preset travel trajectory reflects the traffic flow change trend corresponding to a normal route.

[0131] 9. When it is determined based on the number of times each pair of adjacent road sections passes through an abnormal road section within the first specified time range, the target navigation route is corrected to avoid the abnormal road section to obtain a corrected navigation route;

[0132] 10. If it is determined that the target navigation route does not pass through any abnormal road section based on the number of travel times of each pairwise adjacent road section within the first specified time range, the number of travel times of each pairwise adjacent road section within the specified time range is further queried based on the historical navigation routes within the second specified time range;

[0133] 11. Arrange the number of times each of the two adjacent road sections is traveled within a second specified time range according to the order of the two adjacent road sections on the target navigation route to obtain a second travel number sequence corresponding to the target navigation route. If a trend of a numerical value change in the second travel number sequence does not match a preset travel trajectory, it is determined that the target navigation route passes through an abnormal road section.

[0134] 12. If it is determined that the target navigation route does not pass through any abnormal road section based on the number of times each of the two adjacent road sections passes through the second specified time range, then it is determined that no abnormal road section exists in the target navigation route;

[0135] 13. If the target navigation route passes through an abnormal section based on the number of times each of the two adjacent sections passes through the second specified time range, the target navigation route is corrected to avoid the abnormal section to obtain a corrected navigation route;

[0136] 14. Mark the abnormal road section with an abnormal mark. If the abnormal mark of the abnormal road section is not invalid, for the received navigation route generation request, avoid the abnormal road section and perform route planning for the navigation route generation request.

[0137] The embodiment of the present application is introduced with a specific example. First, the traffic flow between two road sections with a topological connection relationship in the map road network is loaded, that is, the number of passes, including hourly, daily, weekly, monthly, and quarterly traffic. After the route planning is completed and the target navigation route is obtained, the number of passes on the entire target navigation route is calculated. If the link string after route planning is link1, link2, link3, link4, ..., linkn, n-1 transfer flows f1, f2, f3, f4, ..., fn-1 can be obtained, where f1 represents link1, link2, link3, link4, ..., linkn. 2, f2 represents the number of passes between link 2 and link 3, and so on. The system then traverses the number of passes to determine whether there are any passes on either side of the current number that are many times (e.g., 20 times) higher than the current number and are very low. For example, if the sequence of passes is 10, 10, 2000, 2000, 3000, 4, 3, 5, 100, 1000, 2000, 20, 10, 10, and there is a sudden drop in traffic in the middle, then the navigation route passes through a low-traffic area, and the navigation route is abnormal. Hourly traffic is used to more promptly identify dynamic road closures and traffic regulations. For example, if a road was closed an hour ago, daily traffic is still high, and the route problem cannot be identified. Hourly traffic can be used to identify it. The higher the traffic timeliness, the better, such as half-hourly traffic or ten-minute traffic. Hourly traffic mainly identifies problems in hot spots, and remote areas are not covered by hourly traffic. The use of daily, weekly, monthly, and quarterly traffic is also to cover different areas. If the daily traffic transfer flow is very high, there is no need to perform weekly, monthly, and quarterly traffic detection. When the daily traffic coverage is incomplete, try using weekly traffic detection. If the weekly level still cannot cover the problem, use the monthly level, and finally use the quarterly level traffic, which can basically cover the entire road network area. In addition, after detecting an abnormal section, the abnormal section is repaired, such as avoiding the abnormal section and recalculating the route, or, when the route planning has multiple navigation routes, the abnormal section is replaced with a local route that does not pass through the abnormal section, etc. For map navigation services, there may be tens of millions of navigation requests every day. Using the route detection method provided by the embodiment of the present application, millions of abnormal sections can be identified every day. For the identified abnormal sections, the abnormal sections can also be classified, such as identifying the types of multiple abnormal sections, such as traffic restrictions, side roads, gates, road closures, redundant roads, etc. After these sections are marked with these abnormal categories, they can be used in subsequent route planning to improve the problem of unreasonable route planning.

[0138] The embodiments of the present application can be applied to vehicle navigation scenarios. For example, a user can input a starting and ending location through a terminal. The server will then plan a route based on the starting and ending locations based on the map network data, obtaining N navigation routes. The server will then perform an anomaly check on each navigation route using the method provided in the embodiments of the present application. If an abnormal section exists in the navigation route, the server may return a prompt to the terminal, or the server may correct the abnormal section and return the corrected navigation route to the terminal.

[0139] Figure 10 shows a schematic diagram of a route before and after correction in one embodiment. Referring to Figure 10, abnormal sections of the navigation route are identified and corrected to create a more accessible route. The route detection method provided in this embodiment of the application can detect problems in route planning online in real time, avoiding pushing poor routes to users and improving the product experience. Furthermore, automatically identifying data issues can reduce the human effort required for data operations to a certain extent.

[0140] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0141] Based on the same inventive concept, embodiments of the present application also provide a route detection device for implementing the aforementioned route detection method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the route detection device embodiments provided below can be found in the above-described limitations of the route detection method and will not be further elaborated here.

[0142] In one embodiment, as shown in FIG11 , a route detection device 1100 is provided, comprising: an acquisition module 1102 , an adjacent road segment determination module 1104 , a travel count query module 1106 , and an anomaly detection module 1108 , wherein:

[0143] An acquisition module 1102 is configured to acquire a target navigation route to be detected. The target navigation route includes a plurality of sequentially connected road segments;

[0144] An adjacent road segment determination module 1104 is configured to determine a plurality of pairs of adjacent road segments based on a plurality of sequentially connected road segments;

[0145] The travel times query module 1106 is used to query the travel times of two adjacent road sections within a specified time range based on the historical navigation route;

[0146] The anomaly detection module 1108 is used to obtain a passage number sequence corresponding to the target navigation route based on the passage number of each pair of adjacent road sections within a specified time range, and determine whether the target navigation route passes through an abnormal road section based on the passage number sequence.

[0147] In one embodiment, the route detection device 1100 further includes:

[0148] The statistical module is used to obtain a set of historical navigation routes within a specified time range, disassemble each historical navigation route in the historical navigation route set to obtain the pairwise adjacent road segments included in the historical navigation route, obtain a pairwise adjacent road segment set based on the disassembled pairwise adjacent road segments, and for each pair of pairwise adjacent road segments in the pairwise adjacent road segment set, accumulate the number of navigations of the historical navigation routes including the pairwise adjacent road segments within the specified time range to obtain the number of passages of the pairwise adjacent road segments within the specified time range.

[0149] In one embodiment, the travel count query module 1106 is used to query the travel count of two adjacent road segments within a specified time range based on the historical navigation routes within a first specified time range; and is also used to continue to query the travel count of two adjacent road segments within a specified time range based on the historical navigation routes within a second specified time range when it is determined that the target navigation route does not pass through an abnormal road segment based on the travel count of each two adjacent road segments within the first specified time range; wherein the time granularity of the second specified time range is greater than the time granularity of the first specified time range.

[0150] In one embodiment, the anomaly detection module 1108 is used to obtain a first pass count sequence corresponding to the navigation route based on the pass count of each pairwise adjacent road section within a first specified time range, and determine whether the target navigation route passes through an abnormal road section based on the first pass count sequence; and is also used to obtain a second pass count sequence corresponding to the navigation route based on the pass count of each pairwise adjacent road section within a second specified time range, when it is determined that the target navigation route does not pass through an abnormal road section based on the first pass count sequence, and determine whether the target navigation route passes through an abnormal road section based on the second pass count sequence.

[0151] In one embodiment, the route detection device 1100 further includes:

[0152] The correction module is used to correct the target navigation route by avoiding the abnormal section when it is judged that the target navigation route passes through an abnormal section based on the number of times each pair of adjacent sections passes through within a first specified time range, so as to obtain a corrected navigation route; and is also used to correct the target navigation route by avoiding the abnormal section when it is judged that the target navigation route does not pass through an abnormal section based on the number of times each pair of adjacent sections passes through within the first specified time range, and when it is judged that the target navigation route passes through an abnormal section based on the number of times each pair of adjacent sections passes through within a second specified time range, so as to obtain a corrected navigation route.

[0153] In one embodiment, the anomaly detection module 1108 is also used to arrange the corresponding number of passes of each pair of adjacent road sections within a specified time range according to the order of each pair of adjacent road sections on the target navigation route, so as to obtain a pass number sequence corresponding to the target navigation route; when the numerical change trend corresponding to the pass number sequence does not match the preset pass trajectory, it is determined that the target navigation route passes through an abnormal section.

[0154] In one embodiment, the anomaly detection module 1108 is further used to obtain, for each pass number in the pass number sequence, the adjacent pass numbers on both sides of the current pass number in the pass number sequence; when the current pass number is less than a preset threshold and the adjacent pass numbers on both sides of the current pass number are greater than N times the current pass number, determine that the numerical change trend corresponding to the pass number sequence does not match the preset pass trajectory, and determine that the two adjacent road sections corresponding to the current pass number are abnormal road sections, where N is a number greater than 10.

[0155] In one embodiment, the route detection device 1100 further includes:

[0156] A marking module is used to mark abnormal sections when the target navigation route passes through abnormal sections.

[0157] The route planning module is used to plan a route for the received navigation route generation request by avoiding the abnormal road section when the abnormal mark of the abnormal road section is not invalid.

[0158] In one embodiment, the route detection device 1100 further includes:

[0159] The correction module is used to correct the target navigation route when the target navigation route passes through an abnormal section to obtain a corrected navigation route, which does not include the abnormal section.

[0160] In one embodiment, the correction module is also used to determine the two endpoints corresponding to the abnormal section when the target navigation route passes through an abnormal section, regenerate a local path based on the two endpoints, and use the generated local path to replace the abnormal path in the target navigation route to obtain a navigation correction route.

[0161] The route detection device 1100 obtains a target navigation route, determines multiple pairs of adjacent road segments included in the target navigation route, queries the number of passes of the adjacent road segments within a specified time range based on the historical navigation route, obtains a pass number sequence corresponding to the target navigation route based on the number of passes of each adjacent road segment within the specified time range, and determines whether the target navigation route passes through an abnormal road segment based on the pass number sequence. In other words, when determining whether the target navigation route passes through an abnormal road segment, the abnormal road segment is identified based on the flow change trajectory of the target navigation route (reflected by the flow number sequence) rather than the number of passes of each road segment. In particular, when the number of passes of a road segment fails to reflect an abnormality, the abnormal road segment can be detected through the flow change trajectory, thereby improving the rationality and accuracy of route planning, avoiding the waste of computing resources and energy consumption of the route navigation device caused by unreasonable navigation route planning, and improving the user experience of route navigation.

[0162] Each module in the route detection device 1100 may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in hardware form, or may be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0163] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in Figure 12. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer-readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium. The database of the computer device is used to store map road network data, abnormal road section data, etc. The I / O interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via a network connection. When the computer-readable instructions are executed by the processor, a route detection method is implemented.

[0164] Those skilled in the art will understand that the structure shown in FIG12 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0165] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the route detection method provided in the embodiment of the present application are implemented.

[0166] In one embodiment, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the steps of the route detection method provided in the embodiment of the present application are implemented.

[0167] In one embodiment, a computer program product is provided, comprising computer-readable instructions, which, when executed by a processor, implement the steps of the route detection method provided in an embodiment of the present application.

[0168] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0169] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile computer-readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0170] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0171] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A route detection method, executed by a computer device, comprising: Acquire a target navigation route to be detected, where the target navigation route includes a plurality of sequentially connected road sections; Determining a plurality of pairs of adjacent road sections based on the plurality of sequentially connected road sections; Querying the number of times each of the two adjacent road sections passes within a specified time range based on historical navigation routes; A passage number sequence corresponding to the target navigation route is obtained based on the number of times each of the two adjacent road sections passes through within a specified time range, and it is determined based on the passage number sequence whether the target navigation route passes through an abnormal road section.

2. The method according to claim 1, characterized in that The method further comprises: Get a collection of historical navigation routes within a specified time range; Decomposing each historical navigation route in the historical navigation route set to obtain any two adjacent road segments included in the historical navigation route; According to the disassembled pairwise adjacent road sections, a pairwise adjacent road section set is obtained; For each pair of adjacent road segments in the set of adjacent road segments, the number of navigations of the historical navigation routes including the adjacent road segments within the specified time range is accumulated to obtain the number of travels of the adjacent road segments within the specified time range.

3. The method according to claim 1, characterized in that The querying of the number of times each of the two adjacent road sections passes within a specified time range based on the historical navigation route includes: Querying the number of times each of the two adjacent road sections passes within the specified time range based on the historical navigation routes within the first specified time range; and When it is determined that the target navigation route does not pass through any abnormal road section based on the number of times each of the two adjacent road sections has been passed through within the first specified time range, the number of times each of the two adjacent road sections has been passed through within the specified time range is continued to be queried based on the historical navigation routes within the second specified time range; wherein the time granularity of the second specified time range is greater than the time granularity of the first specified time range.

4. The method according to claim 3, characterized in that Obtaining a passage number sequence corresponding to the target navigation route based on the passage number of each of the two adjacent road sections within a specified time range, and determining whether the target navigation route passes through an abnormal road section based on the passage number sequence, includes: Obtaining a first pass count sequence corresponding to the navigation route based on the pass counts of each of the two adjacent road sections within the first specified time range, and determining whether the target navigation route passes through an abnormal road section based on the first pass count sequence; and When it is determined that the target navigation route does not pass through an abnormal section based on the first pass count sequence, a second pass count sequence corresponding to the navigation route is obtained based on the pass counts of each of the two adjacent sections within the second specified time range, and whether the target navigation route passes through an abnormal section is determined based on the second pass count sequence.

5. The method according to claim 3, characterized in that The method further comprises: If it is determined that the target navigation route passes through an abnormal section based on the number of times each of the two adjacent sections passes through the first specified time range, the target navigation route is corrected to avoid the abnormal section to obtain a corrected navigation route; and When it is determined that the target navigation route does not pass through an abnormal section based on the number of times each of the two adjacent sections passes through the first specified time range, and when it is determined that the target navigation route passes through an abnormal section based on the number of times each of the two adjacent sections passes through the second specified time range, the target navigation route is corrected to avoid the abnormal section to obtain a corrected navigation route.

6. The method according to claim 1, characterized in that Obtaining a passage number sequence corresponding to the target navigation route based on the passage number of each of the two adjacent road sections within a specified time range, and determining whether the target navigation route passes through an abnormal road section based on the passage number sequence, includes: Arranging the number of passes of each of the two adjacent road sections within a specified time range according to the order of the two adjacent road sections on the target navigation route, to obtain a sequence of the number of passes corresponding to the target navigation route; When the numerical variation trend corresponding to the passage number sequence does not match the preset passage trajectory, it is determined that the target navigation route passes through an abnormal section.

7. The method according to claim 6, characterized in that When the numerical variation trend corresponding to the passage number sequence does not match the preset passage trajectory, determining that the target navigation route passes through an abnormal section includes: For each pass number in the pass number sequence, obtaining adjacent pass numbers on both sides of the current pass number in the pass number sequence; When the current pass count is less than a preset threshold and the adjacent pass counts on both sides of the current pass count are greater than N times the current pass count, it is determined that the numerical change trend corresponding to the pass count sequence does not match the preset pass trajectory, and the two adjacent road sections corresponding to the current pass count are determined to be abnormal sections, where N is a number greater than 10.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: In the case where the target navigation route passes through an abnormal section, marking the abnormal section with an abnormal mark; In a case where the abnormal mark of the abnormal road section is not invalid, for the received navigation route generation request, route planning is performed on the navigation route generation request by avoiding the abnormal road section.

9. The method according to any one of claims 1 to 7, characterized in that The method further comprises: In the case that the target navigation route passes through an abnormal road section, the target navigation route is corrected to obtain a corrected navigation route, and the corrected navigation route does not include the abnormal road section.

10. The method according to claim 9, characterized in that When the target navigation route passes through an abnormal section, the target navigation route is corrected to obtain a corrected navigation route, including: In the case that the target navigation route passes through an abnormal section, two endpoints corresponding to the abnormal section are determined, a local path is regenerated based on the two endpoints, and the generated local path is used to replace the abnormal path in the target navigation route to obtain a navigation correction route.

11. A route detection device, comprising: An acquisition module, configured to acquire a target navigation route to be detected, wherein the target navigation route includes a plurality of sequentially connected road sections; an adjacent road segment determining module, configured to determine a plurality of pairs of adjacent road segments based on the plurality of sequentially connected road segments; A travel frequency query module is used to query the travel frequency of each of the two adjacent road sections within a specified time range based on the historical navigation route; The anomaly detection module is used to obtain a passage number sequence corresponding to the target navigation route based on the passage number of each of the two adjacent road sections within a specified time range, and determine whether the target navigation route passes through an abnormal road section based on the passage number sequence.

12. The device according to claim 11, characterized in that The device further comprises: The statistical module is used to obtain a set of historical navigation routes within a specified time range; disassemble each historical navigation route in the historical navigation route set to obtain pairwise adjacent road segments included in the historical navigation route; obtain a pairwise adjacent road segment set based on the disassembled pairwise adjacent road segments; and for each pair of pairwise adjacent road segments in the pairwise adjacent road segment set, accumulate the number of navigations of the historical navigation routes including the pairwise adjacent road segments within the specified time range to obtain the number of passages of the pairwise adjacent road segments within the specified time range.

13. The device according to claim 11, characterized in that The passage number query module is further configured to query the passage number of each of the two adjacent road sections within the specified time range based on the historical navigation routes within the first specified time range; And when it is determined that the target navigation route does not pass through an abnormal section based on the number of times each of the two adjacent road sections has been passed within the first specified time range, the number of times the two adjacent road sections have been passed within the specified time range is continued to be queried based on the historical navigation routes within the second specified time range; wherein the time granularity of the second specified time range is greater than the time granularity of the first specified time range.

14. The device according to claim 13, characterized in that The anomaly detection module is further configured to obtain a first pass count sequence corresponding to the navigation route based on the pass counts of each of the two adjacent road sections within the first specified time range, and determine whether the target navigation route passes through an abnormal road section based on the first pass count sequence; And when it is determined that the target navigation route does not pass through an abnormal section based on the first pass count sequence, the second pass count sequence corresponding to the navigation route is obtained based on the pass count of each of the two adjacent sections within the second specified time range, and whether the target navigation route passes through an abnormal section is determined based on the second pass count sequence.

15. The device according to claim 13, characterized in that The route detection device further includes: The correction module is used to, when it is judged that the target navigation route passes through an abnormal section based on the number of times each of the two adjacent sections passes through the first specified time range, correct the target navigation route to avoid the abnormal section to obtain a corrected navigation route; and, when it is judged that the target navigation route does not pass through an abnormal section based on the number of times each of the two adjacent sections passes through the first specified time range, and when it is judged that the target navigation route passes through an abnormal section based on the number of times each of the two adjacent sections passes through the second specified time range, correct the target navigation route to avoid the abnormal section to obtain a corrected navigation route.

16. The device according to claim 11, characterized in that The anomaly detection module is further configured to arrange the corresponding number of passes of each of the two adjacent road sections within a specified time range according to the order of the two adjacent road sections on the target navigation route, so as to obtain a pass number sequence corresponding to the target navigation route; and determine that the target navigation route passes through an abnormal section when the numerical change trend corresponding to the pass number sequence does not match a preset pass trajectory.

17. The device according to claim 16, characterized in that The anomaly detection module is further configured to obtain, for each pass number in the pass number sequence, adjacent pass numbers on both sides of the current pass number in the pass number sequence; and when the current pass number is less than a preset threshold and the adjacent pass numbers on both sides of the current pass number are greater than N times the value of the current pass number, determine that the numerical change trend corresponding to the pass number sequence does not match the preset pass trajectory, and determine that the two adjacent road sections corresponding to the current pass number are abnormal road sections, where N is a number greater than 10.

18. A computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, wherein: When the processor executes the computer-readable instructions, the steps of the method according to any one of claims 1 to 10 are implemented.

19. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

20. A computer program product comprising computer-readable instructions, characterized in that When the computer readable instructions are executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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